APEX-1 (DPO)

A from-scratch 1.12B decoder-only LLM, pretrained on ~20B tokens and aligned with DPO.

Full code, architecture writeup, and training diary: github.com/DW-dev-UE/LLM-from-scratch — see BENCHMARK v2 for the full benchmark writeup this card summarizes.

This checkpoint is the DPO-aligned release (pretrain → SFT → DPO).

Model details

Usage

This checkpoint expects the chat template below (system prompt + optional <THINKING> block) — see chat_template.jinja / ARCHITECTURE.md §7.1 in the repo for the exact format.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("YOON1v/Apex-1-DPO", dtype=torch.bfloat16).cuda()
tokenizer = AutoTokenizer.from_pretrained("YOON1v/Apex-1-DPO")

messages = [{"role": "user", "content": "Write a Python function that checks if a number is prime."}]
prompt = tokenizer.apply_chat_template(
    messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
out = model.generate(**inputs, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.9)
print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

Set enable_thinking=True to have the model reason inside <THINKING>...</THINKING> before answering. Per this model's own benchmark (see below), no-thinking mode currently scores higher — thinking mode tends to run long and cut into the answer budget.

Benchmarks

Standard suite (lm-evaluation-harness 0.4.12, bf16), compared against models trained on a similar token budget:

DPO ≥ SFT on every metric above — no alignment tax. BoolQ 62.20 ranks 1st among TinyLlama-1.1B / Pythia-1.0B / OPT-1.3B. Apex-1 matches the 15×-more-trained Pythia-1.0B on commonsense average and beats it on ARC — the most token-efficient entry among same-budget peers, and its HumanEval score is 4.7× Pythia's despite the token gap.

Against the wider 2024–2025 small-model landscape (Llama-3.2-1B/9T, Qwen2.5-1.5B/18T, SmolLM2-1.7B/11T, OLMo-1B/2T), Apex-1 trails — expected given 450–900× less training data, not an architecture gap. Full comparison table, benchmark provenance, and citations: see BENCHMARK-v2.md §5.4 in the repo.

[!NOTE] GSM8K/MBPP train splits partially overlap this model's SFT data (evaluation used the held-out test split); HumanEval is fully uncontaminated. See the repo's ckpt/lm_eval_Apex-1_COMPARISON.md for the raw numbers this card summarizes.

Known limitations

• English-only (no multilingual pretraining data)
• GSM8K / multi-step arithmetic reasoning is very weak (~2%) — this is why the project moved to DPO instead of RLVR for alignment (RLVR needs a non-trivial number of correct samples per group to produce a gradient; this model has too few)
• MMLU sits at random-guess level (~25%), consistent with other 1B-scale models
• Not instruction-tuned for multi-turn conversation beyond what the SFT/DPO mix covers

Training data

• Pretrain: v3-en corpus (fineweb_edu, dclm, finemath4, cosmopedia2, code [python/js/java/c/cpp/sql/shell], finepdfs, wikipedia_en) — 80.8GB, ~20.3B tokens
• SFT: HuggingFaceTB/smoltalk2 English subset, 538K examples (think:no-think ≈ 1:2)
• DPO: 60K preference pairs

License

Apache 2.0.